Multi-objective Reconfiguration of Electrical Distribution Networks Using Multi-Objective Cat Swarm Optimization Algorithm

From Fig 3, it is possible to note that occurs a decrease in the maximum annual demand on the feeder when DGs are connected to the electrical network, except when it is only inserted WGs on the network (the influence in this scenario can be neglected). Table III shows the annual maximum demand of the feeder 1 and its variation in relation to Case

Proposed algorithm consider three types of nodes every type have different initial energy level. normal nodes have E_0 energy. m advanced nodes have a times energy more than normal nodes with E_0 (1+a) energy level. m_0 super nodes have b times energy more than normal nodes with E_0 (1+b) energy level, where a and b are energy factors. As N is the number of total nodes in network, then for number of normal nodes, advanced nodes and super nodes N(1-m) , Nm〖(1-m〗_0) and Nmm_0 in the network, respectively.

VR will advice which branch/unit needs to be considered. If there is more than one branch/unit then how interlinked they are

Assign Infinity distance value to all nodes (except starting) and mark all nodes as unvisited and create a set of the unvisited nodes

The grid, which includes transmission and distribution lines, is a critical aspect of the energy industry that has become antiquated in recent years. The first problem is caused by an increasing demand, 2.5% annually over the last 20 years, leaving the grid overused [gungor]. The second problem is the age of the infrastructure the average age of the power-grid transmission lines are 50–60 years [gungor]. Because the increase in demand and age of the infrastructure coupled with the nonlinear nature of the electric power distribution network have caused serious network congestion issues, which has caused several major blackouts [gungor]. In addition to network congestion, the existing power grid also is out-of-date in the following areas: communications, monitoring, fault diagnostics and automation which further increase the possibility of region-wide system blackout [gungor]. All the aforementioned problems of the “traditional” grid have been increasingly more difficult to meet in the 21st centuries demands, including power-grid integration, system stability and energy storage and an overall decrease in reliability [erol,gungor].

To the best of our knowledge, the variability and uncertainty impacts of excessive renewable energy generation on the unit commitment decisions and real-time dispatch of a micro grid with controllable DGs in the presence of ESS, demand response (DR) and interruption loads have not been investigated before. Thus, this paper examines probabilistic coordination of DERs on micro grid operation considering the associated uncertainties and hourly interruptible loads for a variety of customers. The work presented in this paper can be summarized as follows:

In the past two decades, the problem of optimal power flow (OPF) has received much attention. It is of current interest of many utilities and it has been marked as one of the most operational needs. The OPF problem solution aims to optimize a selected objective function via optimal adjustment of the power system control variables, while at the same time satisfying various equality and inequality constraints.

applied to find out the optimal generation of each unit when the generation cost curves are non-smooth and discontinuous in nature. Most of the PSO algorithms suffer from the problem of premature convergence in the early stages of the search and hence are unable to locate the global optimum. The idea here is to exercise proper control over the global and local exploration of the swarm during the optimization process. The PSO_TVAC based approach for practical non-convex ELD problem is tested on four test systems having different sizes and non-linearities. Out the four, two test systems are with valve point loading effects, one system has POZ and one system has a large dimension with 38 generating units. The PSO_TVAC is found to

In addition, the transmission capacity limits should be considered to optimize the total market cost. In this paper, a new approach based on constrained particle swarm optimization (CPSO) is developed to deal with the multi-product (energy and reserve) and multi-area electricity market dispatch problem. Constrained handling is based on particle ranking and uniform distribution. CPSO method offers a new solution for optimizing the total market cost in a multi-area competitive

Feeder Reconfiguration is basically an optimization problem which has several objectives and operating constraints. A. Merlin and H. Black were the first to implement optimization techniques to solve a Feeder Reconfiguration problem. Optimization is a mathematical tool to obtain an optimized solution. There are numerous techniques to solve an optimization problem where in each technique will yield a different network topology and different optimized solution. Heuristic and meta-Heuristic methods were used in the early 90’s to solve feeder reconfiguration problem. These methods uses many assumptions to yield an optimal solution. However, the solution obtained by heuristic method is considered to be sub-optimal in compared with the

The combinatorial optimization problems such as Travelling Salesman Problem, Minimum Spanning Tree Problem, Vehicle Routing Problem etc. aims at finding an optimal object from a finite set of objects. Brute force methods which include exhaustive search are not feasible for such problems. In recent years many new and interesting methods are applied for the solution of such problems. These methods such as genetic algorithms (GA), Simulated Annealing, Tabu Search, and Neural Networks are inspired from physical and biological processes.

In this dissertation, two approaches viz. Gravitational Search Algorithm (GSA) and Grasshopper Optimization Algorithm (GOA) are implemented to solve various power system optimization problems. GSA is inspired by Newton’s law of gravity and law of motion and can take care of optimality on rough, discontinuous and multi-modal surfaces. The GSA has three main advantages: it can find near optimal solution regardless the initial parameter values, its convergence is fast and it uses few number of control parameters. Moreover, it can handle integer and discrete optimization. GOA methods, population based approach are more common, which mimics the natural phenomenon during the optimization process. They mainly work on the principal of survival of the fittest. The effectiveness & feasibility of the both approaches has been tested on different standard test cases. These test cases have been divided into two alternative models. Test case-I, GSA algorithms are tested on mathematical benchmark functions with known global optimum. In order evaluate the optimization power of GSA various benchmark functions are taken into consideration. These benchmark functions are the classical functions utilized by many researchers. Test case-II ,To examine the applicability and effectiveness of the GSO method, three different test cases of ED

INTRODUCTION- we define a graph as a collection of a number of vertices and edges, and each of its edge basically connects a pair of its vertices. Whereas a tree can be defined as an acyclic graph that is connected. The edges of a graph are assigned with some numerical valuethat may represent the distance between the vertices, the cost or the time etc. that is why it is called aweighted graph. An acyclicgraph that is weightedis known as a weighted tree. The minimum spanning tree (MST) in a weighted graph is called aspanning tree. In this graph the sum of the weights of all the edges is minimum. Multiple MST are present in a graph, but all of theseneedto have unique sum total cost.The problem in constructing MST in an undirected, connected, weighted graph is one of the most known classic optimization problems.Such problems can be solved by greedy or dynamic algorithms within polynomial time.In 1926, first practical problem related to the MST was identified by Boruvka. But now, there are several practically relatedalternatives of the MST problem that were verified to belong to the NP-hard class. For an instance the Degree-Constrained MST problem [2],Bounded Diameter MST (BDMST) problem framed by the researchers named: Nghia and Binh [2], and the Capacitated Minimum SpanningTree problem [2]. Another one called the deterministic MST problem has also been well calculated and many effective algorithms have beenintroduced by many researchers. However, the Kruskal’s algorithm and

A control operator requires for determining the optimal conditions of power network. However, the applying of such approaches is limited by the political and technical reasons. Therefore, second type of multi area economic dispatch models is presented. Decentralized approaches are known as the second types of multi area economic dispatch models. Lagrangian relaxation is the most popular applied to decompose general multi area economic dispatch problem into regional sub problems [lai2016], [li2015] and [ahmadikhatir2014]. The integration of coupling constraints into the objective function is the decomposed way to reach independent planning of each area. A decentralized technique to schedule optimal planning of thermal units and allocate acceptable reserve for a multi area network is presented in [ahmadikhatir2014]. Such technique is based on Lagrangian relaxation. In addition to, other decomposition techniques are applied to solve multi area scheduling problem. In [li2016], a decentralized dynamic economic dispatch model, which is based on modified Benders Decomposition, is presented. However, this algorithm needs a hierarchical control structure with a coordinator. A marginal equivalent decomposition algorithm is deployed to solve multi area optimal power flow

Pertaining to the power grid with the connected bodies of the federal government and private sector the widespread accessibility via cyber space automated systems of power plants, distribution systems in its self could be problematic…i.e. could contribute to incapacitating large areas of power grids system via cyber-attacks. Possible consideration would be to isolate communications from standard cyber space via secured lines (buzzle.com 2016). Less debilitating to grid system are attacks as in one on one sabotage on individual power plants or remote distribution and substations that would interrupt local areas. Assuming power plants have adequate security on site will effectively reduce power plant attacks. In regards to distribution and substations in suburban or rural areas all one has to do would be to take a drive through the countryside or down a neighborhood to understand these are soft targets protect only by a chain-link fence.